Frontiers

Multi-Scale graph wavelet convolutional network for hyperspectral image classification

Hyperspectral images (HSIs) have very high dimensionality and typically lack sufficient labeled samples, which significantly challenges their processing and analysis. These challenges contribute to ...
Frontiers

Graph neural networks in Alzheimer's disease diagnosis: a review of unimodal and multimodal ...

Alzheimer's Disease (AD), a leading neurodegenerative disorder, presents significant global health challenges. Advances in graph neural networks (GNNs) offer promising tools for analyzing multimodal ...
Scientific Research Publishing

Kipf, T.N. and Welling, M. (2016) Semi-Supervised Classification with Graph Convolutional ...

ABSTRACT: The rapid advancements in large language models (LLMs) have led to an exponential increase in survey papers, making it challenging to systematically track and analyze their evolving taxonomy ...
IEEE

Semi-Supervised Learning With Graph Learning-Convolutional Networks

Abstract: Graph Convolutional Neural Networks (graph CNNs) have been widely used for graph data representation and semi-supervised learning tasks. However, existing graph CNNs generally use a fixed ...
Scientific Research Publishing

Chong, Y., Ding, Y., Yan, Q. and Pan, S. (2020) Graph-Based Semi-Supervised Learning: A ...

ABSTRACT: The stochastic configuration network (SCN) is an incremental neural network with fast convergence, efficient learning and strong generalization ability, and is widely used in fields such as ...
IEEE

Semi-supervised Classification of Hyperspectral Image through Deep Encoder-Decoder and ...

Abstract: The hyperspectral image (HSI) classification is a challenging task due to the high dimensional spectral feature space, and a low number of labeled training samples. To overcome these issues, ...